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Nicholay Topin

Carnegie Mellon University

The MineRL BASALT Competition on Learning from Human Feedback


Jul 05, 2021
Rohin Shah, Cody Wild, Steven H. Wang, Neel Alex, Brandon Houghton, William Guss, Sharada Mohanty, Anssi Kanervisto, Stephanie Milani, Nicholay Topin, Pieter Abbeel, Stuart Russell, Anca Dragan

* NeurIPS 2021 Competition Track 

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Towards robust and domain agnostic reinforcement learning competitions


Jun 07, 2021
William Hebgen Guss, Stephanie Milani, Nicholay Topin, Brandon Houghton, Sharada Mohanty, Andrew Melnik, Augustin Harter, Benoit Buschmaas, Bjarne Jaster, Christoph Berganski, Dennis Heitkamp, Marko Henning, Helge Ritter, Chengjie Wu, Xiaotian Hao, Yiming Lu, Hangyu Mao, Yihuan Mao, Chao Wang, Michal Opanowicz, Anssi Kanervisto, Yanick Schraner, Christian Scheller, Xiren Zhou, Lu Liu, Daichi Nishio, Toi Tsuneda, Karolis Ramanauskas, Gabija Juceviciute

* 20 pages, several figures, published PMLR 

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Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods


Feb 25, 2021
Nicholay Topin, Stephanie Milani, Fei Fang, Manuela Veloso


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The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors


Jan 26, 2021
William H. Guss, Mario Ynocente Castro, Sam Devlin, Brandon Houghton, Noboru Sean Kuno, Crissman Loomis, Stephanie Milani, Sharada Mohanty, Keisuke Nakata, Ruslan Salakhutdinov, John Schulman, Shinya Shiroshita, Nicholay Topin, Avinash Ummadisingu, Oriol Vinyals

* 37 pages, initial submission, accepted at NeurIPS. arXiv admin note: substantial text overlap with arXiv:1904.10079 

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Guaranteeing Reproducibility in Deep Learning Competitions


May 12, 2020
Brandon Houghton, Stephanie Milani, Nicholay Topin, William Guss, Katja Hofmann, Diego Perez-Liebana, Manuela Veloso, Ruslan Salakhutdinov

* Accepted as a poster presentation to the 2019 NeruIPS Challenges in Machine Learning workshop (CiML) 

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Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning


Mar 27, 2020
Stephanie Milani, Nicholay Topin, Brandon Houghton, William H. Guss, Sharada P. Mohanty, Keisuke Nakata, Oriol Vinyals, Noboru Sean Kuno

* 10 pages, 2 figures 

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The MineRL Competition on Sample-Efficient Reinforcement Learning Using Human Priors: A Retrospective


Mar 12, 2020
Stephanie Milani, Nicholay Topin, Brandon Houghton, William H. Guss, Sharada P. Mohanty, Oriol Vinyals, Noboru Sean Kuno

* 10 pages, 2 figures 

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MineRL: A Large-Scale Dataset of Minecraft Demonstrations


Jul 29, 2019
William H. Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden Codel, Manuela Veloso, Ruslan Salakhutdinov

* Accepted at IJCAI 2019, 7 pages, 6 figures. arXiv admin note: text overlap with arXiv:1904.10079 

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Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy


Jul 02, 2019
Aaron M. Roth, Nicholay Topin, Pooyan Jamshidi, Manuela Veloso

* 6 pages + 1 page of references 

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Generation of Policy-Level Explanations for Reinforcement Learning


May 28, 2019
Nicholay Topin, Manuela Veloso

* Accepted to Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (2019) 

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The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors


Apr 22, 2019
William H. Guss, Cayden Codel, Katja Hofmann, Brandon Houghton, Noboru Kuno, Stephanie Milani, Sharada Mohanty, Diego Perez Liebana, Ruslan Salakhutdinov, Nicholay Topin, Manuela Veloso, Phillip Wang

* accepted at NeurIPS 2019, 28 pages 

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Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates


May 17, 2018
Leslie N. Smith, Nicholay Topin

* This paper was significantly revised to show super-convergence as a general fast training methodology 

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Exploring loss function topology with cyclical learning rates


Feb 14, 2017
Leslie N. Smith, Nicholay Topin

* Submitted as an ICLR 2017 Workshop paper 

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Deep Convolutional Neural Network Design Patterns


Nov 14, 2016
Leslie N. Smith, Nicholay Topin

* Submitted as a conference paper at ICLR 2017 

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